Learning mobility user choice and demand models from public transport fare collection data

ABSTRACT

A method and system are disclosed for learning a demand model and simulation parameters from validation information. Validation information is received from automatic fare collection systems and trips are reconstructed from the validation information. Origins, destinations, and arrival/departure times are estimated from the reconstructed trips. A demand model is then generated from the origins, destinations, and times. Assignment model parameters are then learned from the received validation information and demand model via iterative simulations. Infrastructure changes are made to a simulated transportation network based on the assignment and demand model using the learned parameters. A simulated response of the transportation network to the infrastructure change is then output.

BACKGROUND

The transportation arts, transportation system arts, the public transitinfrastructure arts, the data processing arts, the data analysis arts,the transportation modeling arts, the predictive arts, and the like.

Improving the transportation of urban areas is a constant challenge forany transportation authority. This challenge is becoming even largerwithin the fast growing mega cities over the world. These fast growingcities will have to solve the issues of traffic congestion and pollutionif they want to stay attractive and continue their economic growth.Planning new infrastructure, in particular for public transit, is oneimportant dimension where transportation authorities can act. Howeverdevelopment of infrastructure is costly, time consuming, and heavy workfor a city. Past such development required therefore a very carefulstudy of the mobility needs and simulation of different alternativesbefore taking such decision.

Currently, the planning of a new transportation infrastructure involvessimulation of several options based on an understanding of the city.This understanding of the city is primarily based upon a custom study.This custom study generally consists of a period of m collection of dataand traveler/citizen surveys, which are then aggregated into a model ofthe city by some expert analysts. This approach is very time consumingand expensive, and therefore, it often has the disadvantages to relyonly on very partial data (only few thousands of people surveyed) ornon-up-to-date data (e.g., global census of 5 years ago). Furthermoreaggregating this data into a model is such an enormous work that dataare often aggregated to a high level of granularity, which allows only amacro simulation of the city mobility dynamics. The cost/precision ratioof simulations built from these custom studies is therefore not optimal.

The fielding of more and more intelligent transportation systems allowsthe collection of millions of transactions which constitute a verydetailed source of information. This is for example, publictransportation ticketing data that are collected from automatic farecollection systems each time a passenger checks-in (and sometimeschecks-out) before boarding (alighting) a vehicle. Currently, thiscollected ticketing data has been very limitedly used for understandingmobility patterns of a city. What is needed is a mechanism that will usethis data in order to learn automatically city models that can then beused for simulation and planning of new infrastructure, as well asincrease significantly the planning cost/precision ratio through theintegration of a much more massive, more up to date set of information,in an automated manner.

Thus, it would be advantageous to provide an effective system and methodfor learning transportation models of an associated transportationsystem, including demand models estimating the geographical location inthe city of a traveler's actual origin and destination to facilitateinfrastructure planning.

INCORPORATION BY REFERENCE

The following references, the disclosures of which are incorporatedherein by reference, in their entirety, are mentioned.

-   Neumann, A., & Balmer, M. (2011). Micro meets macro: A combined    approach for a large-scale, agent-based, multi-modal and dynamic    transport model for Berlin. Berlin: Transport Systems Planning and    Transport Telematics.-   Meignan, D., Simonin, O., & Koukam, A. (2007). Simulation and    evaluation of urban bus-networks using a multiagent approach.    Simulation Modelling Practice and Theory, 15 (6), 659-671.-   Wahba, M. M. (2008). MILATRAS—Microsimulation Learning-based    Approach to Transit Assignment. University of Toronto, Graduate    Department of Civil Engineering.-   Toledo, T., Cats, O., Burghout, W., & Koutsopoulos, H. N. (2010).    Mesoscopic simulation for transit operations. Transportation    Research Part C: Emerging Technologies, 18 (6), 896-908.-   US Patent Application Publication No. 2013/0317742 A1 (Publication    Date: Nov. 28, 2013; application Ser. No. 13/481,042, Filed May 25,    2012), entitled SYSTEM AND METHOD FOR ESTIMATING ORIGINS AND    DESTINATIONS FROM IDENTIFIED END-POINT TIME-LOCATION STAMPS, to    Ulloa Paredes et al.-   US Patent Application Publication No. 2013/0317884 A1 (Publication    Date: Nov. 28, 2013; application Ser. No. 13/480,802, Filed May 25,    2012), entitled SYSTEM AND METHOD FOR ESTIMATING A DYNAMIC    ORIGIN-DESTINATION MATRIX, to Chidlovskii.

BRIEF DESCRIPTION

In one aspect of the exemplary embodiment, a method for learningtransportation models is provided. The method includes receivingvalidation information from at least one ticketing validation system,the validation information including at least one of a timestamp, alocation, or a ticket identification of a traveler on the associatedtransportation network. The method further includes reconstructing eachof a plurality of trips in accordance with the received validationinformation, and estimating an origin and a destination from thereconstructed plurality of trips. In addition, the method for learningtransportation models includes estimating at least one of an arrivaltime or a destination time corresponding to the reconstructed trip, andgenerating a demand model of the associated transportation network inaccordance with the estimated origin, destination and time. Furthermore,at least one of the receiving, reconstructing, estimating, andgenerating is performed by a computer processor.

In another aspect, a system for learning transportation models isprovided. The system includes a demand model construction component thatincludes a trip reconstruction module configured to receive validationinformation from at least one ticketing validation system andreconstruct a plurality of trips on the associated transportationnetwork, an origin and destination estimator configured to estimate anorigin and a destination for each of the plurality of reconstructedtrips, and a time estimator configured to estimate at least one of anarrival time or a destination time for each of the plurality ofreconstructed trips. The system further includes an assignment modelcomponent that includes a traveler model component including a tripplanner, a vehicle model component, and a simulator. In addition, thesystem includes a memory, and a processor in communication with thememory which stores instructions that are executed by the processor togenerate a demand model in accordance with estimated origins,destinations, and times, and retrieve, from an associated database, anassignment model corresponding to the associated transportation network.The instructions stored in memory are also executed by the processor toinput, into the retrieved assignment model, a vehicle model of theassociated transportation network, the vehicle model corresponding tooperations of vehicles on the associated transportation network, andsimulate the assignment model to determine a set of simulated trips. Theinstructions stored in memory also direct the processor to determine adifference between the set of simulated trips and the plurality ofreconstructed trips, iteratively simulate the assignment model withdifferent parameter values responsive to a determined difference, andoutput a set of parameter values for predictive modeling of thetransportation network.

In another aspect, a computer-implemented method for learningtransportation models is provided. The method includes receivingvalidation information from at least one ticketing validation system,the validation information including at least one of a timestamp, alocation, or a ticket identification of a traveler on the associatedtransportation network. The computer-implemented method further includesreconstructing each of a plurality of trips in accordance with thereceived validation information, and estimating an origin and adestination from the reconstructed plurality of trips, wherein for eachuser having historical data associated therewith, determining the originand the destination from a linear combination of a distribution of stopsaround each of a first observed boarding and a last observed alighting.In addition, the method also comprises estimating at least one of anarrival time or a destination time corresponding to the reconstructedtrip, and generating a demand model of the associated transportationnetwork in accordance with the estimated origin, destination and time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B illustrates a functional block diagram of a system forlearning transportation models in accordance with one aspect of theexemplary embodiment.

FIG. 2 illustrates an example of an estimated origin distribution of atrip in accordance with one aspect of the exemplary embodiment.

FIG. 3 illustrates a public transit micro-simulation data flow diagramaccording to one embodiment of the subject application.

FIGS. 4A-4B is a flowchart which illustrates part of the method forlearning transportation models on an associated transportation networkin accordance with one aspect of the exemplary embodiment.

FIGS. 5A-5B illustrate a before and after view of a transportationnetwork relating an infrastructure change simulated in accordance withone aspect of the exemplary embodiment.

FIG. 6 illustrates a view of multiple simulated user positions andbehaviors in accordance with one aspect of the exemplary embodiment.

DETAILED DESCRIPTION

One or more embodiments will now be described with reference to theattached drawings, wherein like reference numerals are used to refer tolike elements throughout. Aspects of exemplary embodiments related tosystems and methods for re-ranking trips on an associated transportationnetwork are described herein.

The systems and methods set forth herein utilize fare collection data,i.e., validation information 137, in order to build both the demandmodel and to learn optimal user choice and demand model parameters.Automatic fare collection data systems provide the full list ofvalidations made by passengers through their use of the transportationsystems. In most of the transportation systems people will validate whenthey enter a vehicle (check-in). Few systems will also ask to validatewhen they leave the vehicle (check-in/checkout). In many systems regularusers will use a transportation medium such as a smart card that willprovide a unique identifier of the user for all the validations sheperforms. Fare collection data constitutes a source of information thatcovers the whole population of users and the whole history ofinteractions of these users with the transportation systems.

Referring now to FIGS. 1A-1B, there is shown a system 100 for learningtransportation models based on ticketing data. The system 100 utilizesthe validation data 137 collected by automatic ticketing validationsystems 136 employed by various components of an associatedtransportation system 134. As used herein, the terms “transportationsystem” and “transportation network” are considered equivalent,referencing a network or system of public transportation such as buses,trams, trains, and the like. It will be appreciated that the variouscomponents depicted in FIGS. 1A-1B are for purposes of illustratingaspects of the exemplary hardware, software, or a combination thereof,are capable of being substituted therein.

It will be appreciated that the transportation model learning system 100of FIGS. 1A-1B is capable of implementation using a distributedcomputing environment, such as a computer network, which isrepresentative of any distributed communications system capable ofenabling the exchange of data between two or more electronic devices. Itwill be further appreciated that such a computer network includes, forexample and without limitation, a virtual local area network, a widearea network, a personal area network, a local area network, theInternet, an intranet, or the any suitable combination thereof.Accordingly, such a computer network comprises physical layers andtransport layers, as illustrated by various conventional data transportmechanisms, such as, for example and without limitation, Token-Ring,Ethernet, or other wireless or wire-based data communication mechanisms.Furthermore, while depicted in FIGS. 1A-1B as a networked set ofcomponents, the system and method are capable of implementation on astand-alone device adapted to perform the methods described herein.

As shown in FIGS. 1A-1B, the transportation model learning system 100includes a computer system 102, which is capable of implementing theexemplary method described below. The computer system 102 may include acomputer server, workstation, personal computer, laptop computer,cellular telephone, tablet computer, pager, combination thereof, orother computing device capable of executing instructions for performingthe exemplary method.

According to one example embodiment, the computer system 102 includeshardware, software, and/or any suitable combination thereof, configuredto interact with an associated user, a networked device, networkedstorage, remote devices, or the like. The exemplary computer system 102includes a processor 104, which performs the exemplary method byexecution of processing instructions 106 which are stored in memory 108connected to the processor 104, as well as controlling the overalloperation of the computer system 102.

The instructions 106 include a demand model construction component 110for generating a demand model 190 that includes a trip reconstructionmodule 112 that receives validation information 137 from one or moreautomatic ticketing validation systems 136 associated with thetransportation network 134. A demand model 190 describes what needs ofdifferent people in terms of transportation, generally represented by anorigin-destination-time (“ODT”) matrix where each row represents auser's travel needs with three dimensions: origin of the trip,destination of the trip, and time (either departure or arrival). Thevalidation information 137 is utilized by the trip extractor 110 toextract a trip 113 that a traveler actually took on the transportationnetwork 134, which trip 113 may then be utilized by the origin anddestination estimator 114, as discussed below. The validationinformation 137 may include, for example, timestamps 140, locations 141,ticket identification 145, and the like. The trip reconstruction module112 may process the received validation information 137 to determine anorigin 142 and a destination 144 for the particular ticketidentification 145 associated with a particular traveler. That is, thetrip reconstruction module 112 may derive, from the timestamps 140 andlocations 141 contained in the validation information 137, i.e., theorigin 142 of the traveler, and deduce, calculate, or otherwisedetermine the corresponding destination 144 of that traveler. The tripreconstruction module 112 may then output a trip 113 that corresponds tothe actual journey the corresponding traveler (i.e., ticketidentification 145) took on the transportation system 134. Variousembodiments of the subject application may utilize the collection ofadditional information from the automatic ticketing validation system136, e.g., validation sequences (set of boarding and alightingvalidations with corresponding timestamps over a particular period oftime), or the like, from which additional information trips 113 may bereconstructed by the trip reconstruction module 112.

The demand model construction component 110 of instructions 106 furtherincludes an origin and destination estimator 114 that estimates theactual origin 115 and destination 116 of each trip 113. According to oneembodiment, a stochastic modeling of location may be utilized toestimate the origin 115 and destination 116 for each trip 113 output bythe trip reconstruction module 112. Such a stochastic model may assumethat the actual origin 115 or destination 116 of a trip 113 isdistributed in a circle around the first boarding or last alighting ofthe trip 113. Suitable examples of such modeling may include, forexample and without limitation, a parameterized function such as auniform or a Gaussian distribution. When the traveller making the trip113 is a regular user, historical data, i.e., the historical profile, ofthe traveller may be used. All instances of travel that can beassociated to the same activity (i.e., a morning trip going from arecurring area of the city to another recurrent area) are then groupedtogether. The origin and destination are then modelled from the linearcombination of the distribution around each observed first boarding/lastalighting. The weight associated to each stop distribution depends onthe frequency of the observation of this stop. FIG. 2 shows an exampleof such distribution obtained. The area denoted by reference number 200illustrates where the probability of the actual origin is high. In thisexample, the user boarded at the central bus top most of the time but wecan also see that there are few instances where she is boarding at otherstops that influence the shape of the distribution.

The demand model construction component 110 of the instructions 106 mayalso include a time estimator 117 that estimates the arrival time and/ordeparture time of a traveler on a trip 113 to or from the estimatedorigin 115 or destination 116. The time estimator 117 first determineswhether to model the trip 113 with respect to its departure time or itsarrival time. It will be appreciated that this choice can be donearbitrarily based on the prior knowledge about the population beingsimulated or through a profiling of the trip 113 reconstructed from thefare collection data. Such profiling would enable determining whetherthe constraint is to arrive before a specific time at the destination(e.g., ‘going to an appointment’ profile) or if the constraint is toleave origin after a specific time (e.g., ‘coming back from school’profile).

The time estimator 117 then estimates the time to leave the origin 115or to arrive at a destination 116 depending upon whether the trip 113 isone for which there is no or there is traveller history. For a trip 113having no traveller/user history, the arrival time may be computed bytaking the observed time of the last alighting and add an estimatedwalking duration to go to the final destination from the vehicle stop.The simulation will use this value to model a distribution of possiblearrival time upper bounded by that computed time, so that no travel planproposed to the traveller during the simulation will arrive later thanthat time. The departure time for a trip 113 with no traveller historymay be computed by taking the observed time of the first boarding andadd an estimated walking duration to go from the origin 115 to thevehicle stop. The simulation will use this value to model a distributionof possible departure times lower bounded by that computed time, so thatno travel plan proposed to the traveller during the simulation willleave earlier than that time.

For a trip 113 having traveller history, the arrival time may becomputed by taking the observed time of the last alighting and add anestimated walking duration to go to the final destination from thevehicle stop for each of the instances of this type of trip in thehistory. The simulation will use a histogram distribution of possiblearrival time built from these computed times. The departure time may becomputed by taking the observed time of the first boarding and add anestimated walking duration to go from the origin 115 to the vehicle stopfor each of the instances of this type of trip in the history. Thesimulation may use a histogram distribution of possible departure timebuilt from these computed times.

The instructions 106 further includes an assignment model component 118,which generates and simulates a corresponding assignment model 192, andwhich includes a traveler model component 120. An assignment model 192,stored in the associated database 130, defines how travel needs will beallocated within a given transportation infrastructure. A large varietyof assignment models exist, from the macro to the micro level. In thecase of public transport modeling with a micro-simulation approach, eachpassenger's and each vehicle's behavior is modeled individually, i.e.,the simulation will simulate the activity of all the vehicles and allthe people travelling during the time interval of the simulation. Thepassenger tries to accomplish her travel objective and her behavior ismodelled through a user choice model (discussed below) that defines allthe decisions that she will have to make during her travel on thetransportation network 134.

The traveler model component 120 includes a trip planner 121 configuredto a set of candidate trips for a user to take, i.e., one or more pathsbetween a selected origin and a selected destination. As will beappreciated, the trip planner 121 may facilitate the determination ofone or more paths between stops 143 associated with the selected originand destination, using the routes 148, schedules 150, durations 154,walking distances 156, stops 143, and services 152 (e.g., bus, train,subway, etc.) associated with a transportation system 134. The tripplanner 121 may determine one or more paths between the selected originand destination as provided by the user and output a top list of trips.It will be appreciated that when determining a trip between the selectedorigin and the destination, the trip planner 121 may use an abstractmodel of the transportation network 134, i.e., a model based on theroutes 148 and schedules 150, as well as other information available tothe trip planner 121 regarding the transportation network 134, userpreferences, and the like. The trip planner 121 may have access to thedata stored on the database 130 in communication with the computersystem 102, may have data related to the transportation system 134,e.g., routes 148, schedules 150, stops 143, services 152, etc., storedon a separate database (not shown) in communication therewith. The tripplanner 121 may further be in communication with an associated database(not shown) that stores previously generated lists of journeys or tripsresponsive to particular origin/destination pairs, and the illustrationin FIGS. 1A-1B is intended as one example implementation of theaforementioned trip planner 121.

As will be appreciated, a suitable trip planner 121 for a publictransportation network 134 is configured to provide information aboutavailable public transport journeys, or trips. Input for the journeyplanner 121 includes an origin, a destination and starting time(departure time), and finds a route from origin to destination usingavailable services of the transportation network 134. It will beunderstood that the choice of routes for the trip planner 121 on apublic transportation network 134 is more constrained than for a roadroute planner, moreover it is not only about choosing a route but alsoabout choosing a service on that route.

The trip planner 121 finds one or more suggested journeys between anorigin and a destination. In accordance with one embodiment, the tripplanner 121 uses a search algorithm to search a graph of nodes(representing access points to the transport network 134) and edges(representing possible journeys between points). Different weightingssuch as distance, cost or accessibility may be associated with eachedge. Searches may be optimised on different criteria, for examplefastest, shortest, least changes, cheapest. They may be constrained forexample to leave or arrive at a certain time, to avoid certainwaypoints, etc. Such a trip planner 121 usually disposes multiplesources of public and private information, including the detailednetwork description, available routes, stop locations, the serviceschedule, walking distances etc. in order to better answer the tripqueries, e.g., queries relating to the trips 113, simulations, and thelike.

The traveler model component of the assignment model component 118stored in memory 106 further includes a user choice model component 122.The user choice model component 122 is configured to model eachtraveler's decisions and behaviors within a simulation. Using an outputof the trip planner 121, the user choice model component 122 simulateswhat will be the choice of the user at all decision points, e.g., whenwalking to a stop: which stop, when to leave, what speed to walk; whenbeing at a stop: which line, which vehicle to board; when being in avehicle: at which stop to leave. In accordance with one embodiment, thevalidation information 137 may be used by the model component todetermine, or learn, the best parameter values for the user choice modelcomponent 122.

The assignment model component 118 further includes a vehicle modelcomponent 123. The vehicle model component 123 generates a vehicle modelof the transportation network 134 regarding the type of vehicle (bus,tram, train, etc.), vehicle movement, vehicle speeds, stop arrivaltimes, stop departure times, and the like.

The assignment model component 118 also includes a simulator 124configured to simulate various aspects of the transportation network134. In one embodiment, the demand model construction model component110 outputs a demand matrix, i.e., the demand model 190, that has beenconstructed from the validation information 137 as discussed above. Theaforementioned validation information 137 may also be used for learningwhich parameter's values to use for the simulation models. The simulator124 may operate in two modes: a training mode where best parametersvalues are selected based on learning from the existing transportationinfrastructure usage and a prediction mode where the selected parametersvalues are used for simulating the transit on a new infrastructure.

In training mode, the demand model generated from the validationinformation of one past period is used. The assignment model component118 stored of the instructions 106 forces the public transportationvehicles to follow the exact same behaviors that were observed for thisperiod. The simulator 124 outputs a set of trips (using theaforementioned trip planner 121) with simulated boarding and alightingevents that can be compared directly with the individual trips 113reconstructed (via the trip reconstruction module 112) from validationinformation 137 when building the demand model. The simulator 124 theniterates with different parameter values and ends once the simulationoutput and the original reconstructed trip are judged to be closeenough. Likewise passenger flows in vehicles can be forced to learnvehicle's parameters. In this phase the period used for building thedemand model may vary from one instance to the other in order to avoidfinding parameters which are only optimal for the training period and toallow cross validating results.

In prediction mode a demand model may be built by the demand modelconstruction component 110 from a past period which is considered to berepresentative of the demand for which simulation of new transportationinfrastructure is desired. In such a mode of operation, both vehiclesand traveler behaviors are simulated by the simulator 124. User choicesare based on the assignment model 192 parameterized with the best valuesobtained in training phase.

Returning to FIGS. 1A-1B, the memory 108 may represent any type ofnon-transitory computer readable medium such as random access memory(RAM), read only memory (ROM), magnetic disk or tape, optical disk,flash memory, or holographic memory. In one embodiment, the memory 108comprises a combination of random access memory and read only memory. Insome embodiments, the processor 104 and memory 108 may be combined in asingle chip. The network interface(s) 120, 122 allow the computer tocommunicate with other devices via a computer network, and may comprisea modulator/demodulator (MODEM). Memory 108 may store data the processedin the method as well as the instructions for performing the exemplarymethod.

The digital processor 104 can be variously embodied, such as by a singlecore processor, a dual core processor (or more generally by a multiplecore processor), a digital processor and cooperating math coprocessor, adigital controller, or the like. The digital processor 104, in additionto controlling the operation of the computer 102, executes instructions106 stored in memory 108 for performing the method outlined in FIGS.4A-4B.

The term “software,” as used herein, is intended to encompass anycollection or set of instructions executable by a computer or otherdigital system so as to configure the computer or other digital systemto perform the task that is the intent of the software. The term“software” as used herein is intended to encompass such instructionsstored in storage medium such as RAM, a hard disk, optical disk, or soforth, and is also intended to encompass so-called “firmware” that issoftware stored on a ROM or so forth. Such software may be organized invarious ways, and may include software components organized aslibraries, Internet-based programs stored on a remote server or soforth, source code, interpretive code, object code, directly executablecode, and so forth. It is contemplated that the software may invokesystem-level code or calls to other software residing on a server orother location to perform certain functions.

The computer system 102 also includes one or more input/output (I/O)interface devices 126 and 128 for communicating with external devices.The I/O interface 126 may communicate with one or more of a displaydevice 130, for displaying information, and a user input device 132,such as a keyboard or touch or writable screen, for inputting text,and/or a cursor control device, such as mouse, trackball, or the like,for communicating user input information and command selections to theprocessor 104. The various components of the computer system 102 may allbe connected by a data/control bus 105. The processor 104 of thecomputer system 102 is in communication with an associated database 133via a link 132. A suitable communications link 132 may include, forexample, the public switched telephone network, a proprietarycommunications network, infrared, optical, or other suitable wired orwireless data transmission communications. The database 133 is capableof implementation on components of the computer system 102, e.g., storedin local memory 108, i.e., on hard drives, virtual drives, or the like,or on remote memory accessible to the computer system 102.

The associated database 133 corresponds to any organized collections ofdata (e.g., validation information, transportation system information(e.g., stops, nodes or stations, schedules, routes), crowdsourcinginformation (e.g., possible paths, durations, frequency of travel,expected transfer times), and the like) used for one or more purposes.Implementation of the associated database 133 is capable of occurring onany mass storage device(s), for example, magnetic storage drives, a harddisk drive, optical storage devices, flash memory devices, or a suitablecombination thereof. The associated database 133 may be implemented as acomponent of the computer system 102, e.g., resident in memory 108, orthe like.

In one embodiment, the associated database 133 may include datacorresponding to an associated transportation system 134, a collectionof routes 148 (a sequence of stops at transportation nodes to be made byan individual vehicle along of a course of travel available on thetransportation system 134), schedules 150 for each of these routes 148,transportation nodes, such as stations or stops 143, paths (i.e., asequence of successive origins and destinations between a first originand the last destination, services 152, duration 154, walking distances156, demand models 190, and assignment models 192. For example, in thecase of a public transportation system, the associated database 133 mayinclude information relating to the public transportation system 134such as public transportation routes (e.g., a known sequence ofscheduled stops by an individual bus, subway, train, etc.) 148,schedules 150 that pertain to the arrival/departure times of buses,trams, subways, etc., of the transportation system 134 for these routes148, public transportation stops (or stations) 143, i.e., fixedlocations, or nodes, that are linked by the transportation system 134,and a set of paths between two stops 143, each path being associablewith one or more transportation routes 148 (e.g., a path may includebeginning a journey by train at a first station (origin), riding thetrain (along the train's scheduled route) to a second station,transferring to a bus at the second station, and riding the bus (alongthe bus's scheduled route) so as to alight at a third station(destination)).

The database 133 may further include validation information 137collected from the automatic ticketing validation system 136. Forexample, in the context of a public transportation system, thevalidation information 137 may pertain to travelers, such as eachticket's unique identification 145 (e.g., the ticket identification 145may be derived from a smart card, a transit card, transit ticket, or thelike, that cannot be rewritten or otherwise altered by the user(anti-counterfeiting properties)), locations 141 (origin and destinationlocations), sequence tags (first use, correspondence use, etc.,),timestamps 140 associated with the transfer times between an origin anda destination for that particular validation sequence. That is, eachvalidation sequence may include the time of entry of the traveler on thepublic transportation network 134 along with the corresponding location141 or route 148 (i.e., stop 143 on the route 148) at which the travelerboarded or alighted, and the like. While each traveler on a publictransport system is generally a person, travelers of other networkedtransportation systems may include goods or other inanimate objects.

Each location 141 of the validation information 137 may include one ormore of a route identifier e.g., a route number, a transportation nodeidentifier, e.g., a stop number, an address, GPS coordinates, or othergeographical identification information associated with the location.The time component of the stamp 140 may include one or more of a time ofday, a day, a date, or other temporal information corresponding to thetimestamp 140. The validation information 137 collected and used in thevarious embodiments contemplated herein may thus be ticketing data,collected via usage of prepaid cards, single use transit tickets,reloadable transit cards, or other ticketing devices. The abovereferences sequence tags may reflect where or when in the a particularvalidation sequence the boarding or alighting of the traveler occurred,i.e., a “First” tag may indicate the first use of the ticketidentification 145, whereas a “Correspondence” tag may indicate thesecond and each subsequent use of the ticket identification 145 within aspecified time period.

The validation information 137 may be collected from a plurality oflocations on the associated transportation system 134. As brieflydiscussed above, each of these locations may correspond to one of afinite set of locations (e.g., stations, stops, etc.) connected by thetransportation system 134, or vehicles which travel along routes 148 ofthe transportation system 134. It will be appreciated that thecollection of such information 137 may be performed by ticket validationmachines for fare collection, i.e., automatic ticket validation systems136 at each respective location, such as smart card readers, magneticcard readers, input terminals, ticket dispensers, ticket readers, andthe like. It will be appreciated that such automatic ticket validationsystems may be implemented at stations, on vehicles, etc., and mayrepresent automatic fare collection subsystems.

Various travelers on the transportation system 134 may usetransportation cards/tickets. Such cards/tickets may be used to pay foror otherwise enable travel on the transportation system 134 and thus arescanned, read, inserted in, or otherwise detected by the automaticticket validation systems 136 as the travelers travel through thetransportation system 134 from an origin location 142 to a destinationlocation 144. Such transportation cards may include smart card-likecapabilities, e.g., microchip transmissions, magnetically stored data,and the like. In such embodiments, the automatic ticket validationsystems 136 communicate validation information 136 to the computersystem 102 via link 146. A suitable communications link 146 may include,for example, the public switched telephone network, a proprietarycommunications network, infrared, optical, or any other suitable wiredor wireless data transmission communications.

It will be appreciated that additional information may be collected bythe automatic ticket validation systems 136 corresponding to ticketingoperations including transportation usage data, ticketing receipt data,congestion data, and the like. According to one embodiment, both entriesand exits of passengers on and off vehicles or nodes of thetransportation system 134 may be collected as validation information137. Entry-only systems, in contrast, may allow for the collection ofelectronic validation records pertaining only to the entry of a traveleron a vehicle or at an origin node of the transportation system 134.While the destinations of travelers in an entry-exit system may bediscernible from the automatic ticketing validation data, i.e., thevalidation information 137 collected by the automatic ticket validationsystems 136, destinations of passengers in an entry-only automaticticketing validation system 136 may be discerned through inferencesbased upon non-validation data (e.g., transportation system routes andschedules, event occurrences (sports, concerts, etc.), or the like) andtraveler assumptions.

The systems and methods described herein may use one-trip tickets aswell as prepaid cards, which are reflected in the ticket identification145 included in the collected validation information 137. A one tripticket may have a fixed validation time, i.e., a period of time duringwhich the ticket remains valid for use by a traveler. For example, inentry-only systems, the time during which the ticket is valid may belimited to 1 hour from the time of issuance/purchase, during which timetravelers may change vehicles within the transportation network 134without incurring an additional charge. The first validation of such aticket may be identified by a sequence tag indicating ‘First’, whereasthe second and subsequent validations during this validation time may beidentified by a sequence tag indicating ‘Correspondence’. The automaticticketing validation systems 136 may allow for the use of multiple entrycards, which may provide for multiple entries by a traveler andlong-term permanent cards to requesting travelers. It will beappreciated that the use of multiple entry cards may permit trackingtraveling data of each card holding traveler, as well as allowing fortime-based analysis of such travelers.

The automatic ticketing validation systems 136 may allow for locationidentification, corresponding to the entry or the entry and exit of atraveler. For example, the automatic ticketing validation systems 136may enable each validation of a ticket to include a ticket ID 145, atimestamp 140 and a correspondence tag. Additionally, the automaticticketing validation systems 136 can use automatic vehicle locationsubsystems to associate a ticket validation with the publictransportation route 148, stop identifier (e.g., stop 143) anddirection. Other methods for collecting validation information 137 mayalternatively or additionally be used, including, mobile communicationevents, e.g., time-stamped antenna authentication sequences or otherobservations of the intersecting of scheduled activities and travelerschedules. The ticket validations, i.e., the validation information 137collected in the automatic ticketing validation systems 136 may provideinformation for understanding the traveler flows in the transportationnetwork 134. Information in a typical installation can be analyzed inorder to provide valuable insights for the transit and publictransportation agencies and assist in decision making processes.

The validation information 137 associated with the implementation ofFIG. 1 is for example purposes only. Other applications outside of thepublic transportation example are also contemplated. For example,toll-road monitoring and management systems may also take advantage ofthe subject systems and methods, whereby validation information 137 iscollected at toll-booths, upon entry and exit of a vehicle with respectto the associated toll road. Other embodiments, e.g., hospitalmonitoring of patient/employee entries and exits, secure facilitymonitoring, and the like, are also contemplated.

Turning now to FIG. 3, there is shown a flow diagram 300 illustrating anexample implementation of a public transit micro-simulation inaccordance with embodiments of the subject application. In accordancewith the flow diagram 300 of FIG. 3, it will be appreciated that thesystem 100 of the subject application may be utilized for a publictransit simulation system which models at a micro-level the passengers.Accordingly, each individual passenger trip 113 with all the possiblebehaviors of the passenger is modelled.

As illustrated in FIG. 3, the demand modelling component 110 depicts adistinct improvement over a classical approach where a static origindestination matrix is built from a survey. In the subject application, astochastic model of demand is learned from validation information 137that will generate a sample of an origin destination matrix each time asimulation is launched. It utilizes a user trip reconstruction module112 that translates validations 137 collected by a fare collectionsystem 136 into individual trips 113 as described above. Then, using thesame previously described method, the demand model 190 is generatedestimating origin 115, destination 116, and time.

The second component of FIG. 3, the transit assignment modellingcomponent 118 utilizes a traveler model component 120 that will be incharge of simulating each traveler's decisions and behaviors within thesimulation. The schedule 150 of the transportation network 134 is usedby the vehicle model component 302 to provide decision/event inputs tothe micro-simulation component 304. The trip planner component 121proposes some alternative paths according to the public infrastructureavailable. The user choice model component 122 simulates what will bethe choice of the user at all decisions points, e.g., when walking to astop: which stop, when to leave, what speed to walk; when being at astop: which line, which vehicle to board; and when being in a vehicle:at what stop to leave. The traveler model component 120 further utilizesthe demand model 190 and events/decisions from the simulation to refinethe simulation. Thereafter, simulated individual trips 306 are output bythe assignment model component 118. Accordingly, the validationinformation can be used in training mode in order to learn the bestparameter values for the demand model 190, the trip planner 121 and theuser choice model 122.

Turning now to FIGS. 4A-4B, there is shown a flowchart 400 illustratinga method for learning transportation models on an associatedtransportation network according to one embodiment. The methodology ofFIGS. 4A-4B begins at 402, whereupon validation information 137 isreceived from at least one automatic ticketing validation systems 136,said validation information may include timestamps 140, locations 141(e.g., stops 143) and ticket identifications 145. At 404, each trip 113undertaken by a traveler, as indicated by the validation information137, is reconstructed. Each trip 113 includes at least a timestamp 140,an origin 142, and a destination 144. The trip 111 may be reconstructedby the trip reconstruction module 112 from validation information 137collected by one or more automatic ticketing validation systems 136.

At 406, origins 115 and destinations 116 are estimated via the originand destination estimator 114 of the computer system 102 from thereconstructed trips 113. As discussed above, the estimation may involvea parameterized function such as a uniform or Gaussian distribution, mayutilize historical traveler data (if known), or the like. At 408, a timeis assigned to each trip via the time estimator 117. As previouslydiscussed, the time may be the arrival time or the departure time, andmay be determined differently for known and unknown users. A demandmodel 190 is then generated at 410 from the estimated origin,destination, and time, as was discussed in greater detail above.

A determination is then made at 412 whether a simulation to be performedcorresponds to a learning mode or a predictive mode. Upon adetermination at 412 that a learning mode is selected, operationsproceed to 414, whereupon an assignment model 192 is retrieved fromstorage 133 corresponding to the transportation network 134 by theassignment model component 118. At 416, vehicle modeling is performed byforcing the vehicles of the transportation network 134 to operate andbehave in accordance with the observed data, i.e., the vehicle operateas they did during the time period of the validation information 137collected to generate the demand model 190. At 418, the simulator 124operates to run a simulation to acquire a simulated set of origins anddestinations. A set of trips with simulated events (based upon thesimulated origins and destinations) is then output at 420.

At 422, the set of simulated trips is compared to the set ofreconstructed trips 113 used to generate the demand model 190. Adetermination is then made at 424 whether the two sets are close enoughin values. This closeness may correspond to a threshold differencebetween the individual trips, between the sets as a whole, or myriadother arbitrary or objective thresholds. Upon a determination that thetrips are not close enough, operations progress to steep 426, whereuponone or more parameter values associated with the simulation are iteratedand operations return to step 418 for additional simulations. Once thesets of trips are judged to be close enough, operations proceed to step428, whereupon the parameter values are stored, i.e., the simulationmodel parameters are learned, and operations return to step 412.

Returning to step 412, upon a determination that prediction mode hasbeen selected, operations progress to step 430. At 430, an assignmentmodel 192 of the transportation network 134 is retrieved from associatedstorage 133. At 432, one or more infrastructure changes are made to thetransportation network 134. It will be appreciated that the selection ofinfrastructure changes may include, for example and without limitation,route change, new stop added, new vehicle added, stop removed, new trainline added, additional vehicles on same route, new train station, ormyriad other additions or deletions to the infrastructure of theunderlying transportation network 134. It will further be appreciatedthat such selection may be made via the user input devices 130 and 131,a remote terminal (not shown), or the like. The parameter values arethen retrieved from associated storage 133 at 434. In accordance withone embodiment, the parameter values correspond to the previouslylearned assignment model 192 and may correspond to a particular timeperiod, e.g., a day of the week, a week of the month, a month of theyear, or myriad variations thereof.

At 436, vehicle behavior is then simulated on the transportation network134 responsive to the infrastructure change(s). Traveler behavior, i.e.,user choices, are then simulated at 438 on the assignment modelparameterized with the best values learned at 428 and retrieved at 434.A simulated response of the transportation network 134 to theinfrastructure change is then output by the system 102 at 440.

FIGS. 5A and 5B provide illustrations 500 and 502 depicting thesimulated trips according to one embodiment of the subject application.As shown in FIGS. 5A-5B, comparative views of the vehicle load aredepicted, aggregated as a heat map over the same time period in twodifferent scenarios. Heat map 500 of FIG. 5A represents the vehicle loadof the transportation network 134 that was observed in the realoperations of the network 134. FIG. 5B illustrates a heat map 502 thatvisualizes a simulated response of the network 134 obtained when aninfrastructure change, i.e., removal of a tram line, was made during thesame reference period.

FIG. 6 illustrates a screenshot map 600 of an example transportationnetwork 134 provided to an administrator or manager of the network 134.It will be appreciated that as the simulated output of the system 100reconstructs the whole trips 113 of each individual traveler, a dynamicrepresentation of each individual trajectory 602 may be illustrated on amap 600 of the transportation network 134. In one exampleimplementation, an administrator or other suitable personnel associatedwith management of the transportation network 134 may, via the display130 and user input device 131, modify the illustration 600 to visualizethe position and behavior of each simulated user 602 at any moment oftime during the studied period.

It will be appreciated that the systems and methods disclosed hereinprovide a distinct improvement over current operations in that thesystems and methods set forth above do not require a separate datacollection phase, e.g., surveys or the like, in order to produce asimulation. Data gathering represents approximately one-third of thebudget of planning departments of transportation authorities. Inaddition to a large reduction in the cost associated with infrastructurechange simulations, the systems and methods disclosed herein provide afaster processing time, as the data collection of current systems andmethods may require a year or more, depending upon the scale of thetransportation network.

Furthermore, the systems and methods described herein provides up todate simulations, as the data used in the subject application is basedupon validation information 137 collected by the transportation network134 daily, resulting in an up-to-date representation of the demand uponthe network 134. Similarly, traveler behaviors can be calibrateddynamically taking into consideration the most recent evolutions in thechange of behavior, resulting in a more accurate simulation output.

In addition, the systems and methods employed herein may utilizehistorical traveler data, as the validation information 137 is collectedand stored on a daily basis. This is a distinct improvement over surveydata, as survey data merely represents a snapshot of mobility on atransportation network 134. The collected historical traveler data maybe used to produce a more sophisticated model, thereby increasing theaccuracy of the simulation output. It will be appreciated that thenumber of identified users with a payment history increases as paymenttechnologies become cheaper and integrated into existing mediums (suchas smart phones or credit cards).

Finally another improvement over existing simulation methodologies isthat the validation information 137, i.e., fare collection data, coversthe whole population of public transport users, i.e., travellers on thetransportation network 134. This is particularly important for thetraining phase. In state of the art approaches, calibration of theparameters is done using a very limited amount of samples with a highrisk of overfitting issues. The subject systems and methods providecomplete coverage, thereby allowing for decomposition andcross-validation on time, space and population which reduces a lot theuncertainty of generalization and again has some positive effect on thesimulation accuracy.

The systems and methods described above have been used together with aself-implemented public transit simulation system. This transportationsystem is implemented in PYTHON with a distributed framework allowing alarge scale distribution of the traveler modelling component 120 inorder to simulate transportation systems of real scale, i.e. hundredthousands or millions of individual trips in a day.

One critical dimension with this approach is the computationalcomplexity involved with a micro-simulation at a metropolitan areascale. The self-implemented public transit simulation system is run witha public transportation network which includes around 2700 vehicles trip(buses and trams) and 100,000 individual passenger trips over one day.It can be qualified as a small to medium size network.

The system is run in a distributed computing infrastructure with around200 processing cores. In such infrastructure one simulation of a fullday in one core is computed within 1 to 2 minutes. Such fast processingtime allows decision makers to test various planning alternatives in avery responsive way. The training phase, as will be understood, requiresadditional time, however this additional time is still orders ofmagnitude less than a survey collection would require, e.g., roughly 1week of computation for training enable around 2,000,000 iterations.

The method illustrated in FIGS. 3-4B may be implemented in a computerprogram product that may be executed on a computer. The computer programproduct may comprise a non-transitory computer-readable recording mediumon which a control program is recorded (stored), such as a disk, harddrive, or the like. Common forms of non-transitory computer-readablemedia include, for example, floppy disks, flexible disks, hard disks,magnetic tape, or any other magnetic storage medium, CD-ROM, DVD, or anyother optical medium, a RAM, a PROM, an EPROM, a FLASH-EPROM, or othermemory chip or cartridge, or any other tangible medium from which acomputer can read and use.

Alternatively, the method may be implemented in transitory media, suchas a transmittable carrier wave in which the control program is embodiedas a data signal using transmission media, such as acoustic or lightwaves, such as those generated during radio wave and infrared datacommunications, and the like.

The exemplary method may be implemented on one or more general purposecomputers, special purpose computer(s), a programmed microprocessor ormicrocontroller and peripheral integrated circuit elements, an ASIC orother integrated circuit, a digital signal processor, a hardwiredelectronic or logic circuit such as a discrete element circuit, aprogrammable logic device such as a PLD, PLA, FPGA, Graphical card CPU(GPU), or PAL, or the like. In general, any device, capable ofimplementing a finite state machine that is in turn capable ofimplementing the charts shown in FIGS. 3-4B can be used to implement themethod for crowdsourcing a transportation network.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomany other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.

What is claimed is:
 1. A method for learning transportation models of anassociated transportation network, comprising: receiving validationinformation from at least one ticketing validation system, thevalidation information including at least one of a timestamp, alocation, or a ticket identification of a traveler on the associatedtransportation network; reconstructing each of a plurality of trips inaccordance with the received validation information; estimating anorigin and a destination from the reconstructed plurality of trips;estimating at least one of an arrival time or a destination timecorresponding to the reconstructed trip; and generating a demand modelof the associated transportation network in accordance with theestimated origin, destination and time, wherein at least one of thereceiving, reconstructing, estimating, and generating is performed by acomputer processor.
 2. The method of claim 1, further comprising:retrieving, from an associated database, an assignment model of theassociated transportation network; simulating the transportation networkin accordance with the assignment model to acquire origins, destinationsand times; and reconstructing a set of simulated trips derived from thesimulated origins, destinations, and times.
 3. The method of claim 2,further comprising: comparing the set of simulated trips to acorresponding set of reconstructed trips; determining, from thecomparing, a difference between the set of simulated trips and thecorresponding set of reconstructed trips; and in response to thedetermined difference, iteratively adjusting at least one parametervalue associated with the assignment model.
 4. The method of claim 3,further comprising: selecting at least one new infrastructure for theassociated transportation network; parameterizing the assignment modelin accordance with the at least one iteratively adjusted parametervalue; simulating the parameterized assignment model in accordance withthe selected at least one new infrastructure; and outputting a simulatedresponse of the associated transportation network corresponding to aresult of the parameterized assignment model simulation.
 5. The methodof claim 4, wherein simulating the parameterized assignment modelfurther comprises: simulating, for each of a plurality of vehicles ofthe associated transportation network, vehicle behavior responsive tothe at least one new infrastructure; simulating, for each of a pluralityof travelers, a plurality of decision points corresponding to travel onthe associated transportation network responsive to the at least one newinfrastructure; and outputting the simulated response of the associatedtransportation network in accordance with the simulated vehicle behaviorand the simulated traveler decision points.
 6. The method of claim 5,wherein the plurality of decision points include a stop, a time to leavefor arrival at a stop, a speed to walk to a stop, a line at a stop, avehicle to board at a stop, a stop from which to depart a vehicle. 7.The method of claim 1, wherein the associated transportation network isa public transportation network.
 8. The method of claim 1, wherein theestimating an origin and destination further comprises for each userhaving historical data associated therewith, modelling an origin and adestination from a linear combination of a distribution of stops aroundeach of a first observed boarding and a last observed alighting, whereina weight assigned to each stop distribution is determined in accordancewith a frequency of observations of the stop.
 9. The method of claim 1,wherein estimating the time further comprises: for each user without atravel history, determining an arrival time by adding to an observedtime of a last alighting an estimated walking duration from a stopcorresponding to the last alighting to a final destination, anddetermining a departure time by adding to an observed time of a firstboarding an estimated walking duration from an origin to a stopassociated with the first boarding; and for each user with a travelhistory, determining an arrival time by adding to an observed time of alast alighting an estimated walking duration from a stop associatedcorresponding to the last alighting to a final destination for eachinstance of a trip in the travel history corresponding thereto, anddetermining a departure time by adding to an observed time of a firstboarding an estimated walking duration from an origin to a stopcorresponding to the first boarding for each instance of a trip in thetravel history corresponding thereto.
 10. A computer program productcomprising a non-transitory recording medium storing instructions, whichwhen executed on a computer causes the computer to perform the method ofclaim
 1. 11. A system comprising memory storing instructions forperforming the method of claim 1, and a processor in communication withthe memory which implements the instructions.
 12. A system for learningtransportation models on an associated transportation network,comprising: a demand model construction component comprising: a tripreconstruction module configured to receive validation information fromat least one ticketing validation system and reconstruct a plurality oftrips on the associated transportation network; an origin anddestination estimator configured to estimate an origin and a destinationfor each of the plurality of reconstructed trips, and a time estimatorconfigured to estimate at least one of an arrival time or a destinationtime for each of the plurality of reconstructed trips; an assignmentmodel component comprising: a traveler model component including a tripplanner, a vehicle model component, and a simulator; a memory; and aprocessor in communication with the memory which stores instructionsthat are executed by the processor to: generate a demand model inaccordance with estimated origins, destinations, and times, retrieve,from an associated database, an assignment model corresponding to theassociated transportation network, input, into the retrieved assignmentmodel, a vehicle model of the associated transportation network, thevehicle model corresponding to operations of vehicles on the associatedtransportation network, simulate the assignment model to determine a setof simulated trips, determine a difference between the set of simulatedtrips and the plurality of reconstructed trips, iteratively simulate theassignment model with different parameter values responsive to adetermined difference, and output a set of parameter values forpredictive modeling of the transportation network.
 13. The system ofclaim 12, wherein the memory further stores instructions executed by theprocessor to: select at least one new infrastructure for the associatedtransportation network; parameterize the assignment model in accordancewith the at least one iteratively adjusted parameter value; simulate theparameterized assignment model in accordance with the selected at leastone new infrastructure; and output a simulated response of theassociated transportation network corresponding to a result of theparameterized assignment model simulation.
 14. The system of claim 13,wherein the memory further stores instructions executed by the processorto: simulate, for each of a plurality of vehicles of the associatedtransportation network, vehicle behavior responsive to the at least onenew infrastructure; simulate, for each of a plurality of travelers, aplurality of decision points corresponding to travel on the associatedtransportation network responsive to the at least one newinfrastructure; and output the simulated response of the associatedtransportation network in accordance with the simulated vehicle behaviorand the simulated traveler decision points.
 15. The system of claim 14,wherein the plurality of decision points include a stop, a time to leavefor arrival at a stop, a speed to walk to a stop, a line at a stop, avehicle to board at a stop, a stop from which to depart a vehicle. 16.The system of claim 12, wherein the origin and destination estimator isfurther configured to: for each user having historical data associatedtherewith, model an origin and a destination from a linear combinationof a distribution of stops around each of a first observed boarding anda last observed alighting, wherein a weight assigned to each stopdistribution is determined in accordance with a frequency ofobservations of the stop.
 17. The system of claim 12, wherein the timeestimator is further configured to: for each user without a travelhistory, estimate an arrival time by adding to an observed time of alast alighting an estimated walking duration from a stop correspondingto the last alighting to a final destination, and estimate a departuretime by adding to an observed time of a first boarding an estimatedwalking duration from an origin to a stop associated with the firstboarding; and for each user with a travel history, estimate an arrivaltime by adding to an observed time of a last alighting an estimatedwalking duration from a stop associated corresponding to the lastalighting to a final destination for each instance of a trip in thetravel history corresponding thereto, and estimate a departure time byadding to an observed time of a first boarding an estimated walkingduration from an origin to a stop corresponding to the first boardingfor each instance of a trip in the travel history corresponding thereto.18. A computer-implemented method for learning transportation models ofassociated public transportation network, comprising: receivingvalidation information from at least one ticketing validation system,the validation information including at least one of a timestamp, alocation, or a ticket identification of a traveler on the associatedtransportation network; reconstructing each of a plurality of trips inaccordance with the received validation information; estimating anorigin and a destination from the reconstructed plurality of trips,wherein for each user having historical data associated therewith,determining the origin and the destination from a linear combination ofa distribution of stops around each of a first observed boarding and alast observed alighting; estimating at least one of an arrival time or adestination time corresponding to the reconstructed trip; and generatinga demand model of the associated transportation network in accordancewith the estimated origin, destination and time.
 19. Thecomputer-implemented method of claim 18, further comprising: retrieving,from an associated database, an assignment model of the associatedtransportation network; simulating the transportation network inaccordance with the assignment model to acquire origins, destinationsand times; reconstructing a set of simulated trips derived from thesimulated origins, destinations, and times; comparing the set ofsimulated trips to a corresponding set of reconstructed trips;determining, from the comparing, a difference between the set ofsimulated trips and the corresponding set of reconstructed trips; and inresponse to the determined difference, iteratively adjusting at leastone parameter value associated with the assignment model.
 20. Thecomputer-implemented method of claim 19, further comprising: selectingat least one new infrastructure for the associated transportationnetwork; parameterizing the assignment model in accordance with the atleast one iteratively adjusted parameter value; simulating, for each ofa plurality of vehicles of the associated transportation network,vehicle behavior responsive to the at least one new infrastructure;simulating, for each of a plurality of travelers, a plurality ofdecision points corresponding to travel on the associated transportationnetwork responsive to the at least one new infrastructure andparameterized assignment model; and outputting the simulated response ofthe associated transportation network in accordance with the simulatedvehicle behavior and the simulated traveler decision points.